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相关概念视频

Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

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Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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Elastic Collisions: Introduction01:00

Elastic Collisions: Introduction

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An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
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Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
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Virtual Work for a System of Connected Rigid Bodies01:06

Virtual Work for a System of Connected Rigid Bodies

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Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is zero. This allows the calculation of the resulting forces when a system undergoes a virtual displacement. When attempting to analyze such a system, first, use a free-body diagram, where an independent coordinate represents the configuration of the links, and mark its deflected position resulting from the positive virtual displacement.
Next,...
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学习集体变量与合成数据增强通过物理灵感的地球测量干预.

Soojung Yang1, Juno Nam2, Johannes C B Dietschreit2,3

  • 1Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

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概括
此摘要是机器生成的。

本研究引入了一种新的无模拟方法,用于生成蛋白质折叠模拟数据. 这种方法通过创建现实的过渡路径而提高采样效率,而不需要实际的过渡状态样本.

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科学领域:

  • 计算化学是一种计算化学.
  • 生物物理学的生物物理.
  • 分子动力学模拟的模拟.

背景情况:

  • 在分子动力学模拟中,增强的采样技术通常依赖于集体变量 (CV) 来研究罕见事件,如蛋白质折叠.
  • 鉴定有效的简历是具有挑战性的,因为事先对事件的途径的知识有限.

研究的目的:

  • 开发一个无模拟的数据增强策略,以提高增强采样技术的效率.
  • 在不需要真实过渡状态样本的情况下,生成现实的蛋白质折叠过渡数据.

主要方法:

  • 一个数据增强策略,使用灵感来自物理的指标来生成地测插入.
  • 创建模仿蛋白质折叠过渡的数据.
  • 在基于回归的CV模型学习中利用插值进度参数.

主要成果:

  • 成功生成了类似蛋白质折叠过渡的无模拟数据.
  • 通过增强数据,证明了改进基于分类器的方法的潜力.
  • 展示了回归式学习对CV模型的有用性.

结论:

  • 拟议的数据增强策略提高了分子动力学模拟中的采样效率.
  • 这种方法为生成关键的过渡数据提供了可行的替代方案,当真实样本稀缺时.
  • 提高集体变量模型的准确性和适用性,用于研究复杂的生物分子事件.